Presentation given by Darius Bazazi, GeoPlace, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
2. About me
Background in environmental science
MSc in GIS, 2013
Now Spatial Data Analyst at GeoPlace (National Address Gazetteer)
3. About GeoPlace
Owned by Local Government Association / Ordnance Survey
Definitive national address & street gazetteers (datasets)
380 local authorities working together across GB – includes One Scotland Gazetteer (OSG) managed
by ThinkWhere
AddressBase products used by emergency services, government agencies, insurance companies
4. AddressBase features
Point data for every address in Great
Britain
Full postal address and many other
attributes
Unique Property Reference Number
(UPRN) for every property
Property lifecycle – includes historical & in
planning
Detailed property classification
Cross references to other OS products
e.g. MasterMap, ITN
5. Address data in Flood Management
Properties located in different flood zones
Allows emergency services target resources in flood event
6. Importance of address data...
Ofwat: Thames Water misreported the number of properties at
high risk of sewer flooding between 2005 and 2010
"This meant that more properties were recorded at higher risk of
sewer flooding than there was evidence to support“
While this fine relates to sewers it is crucial that accurate property
data is available for these types of analyses
7. Project overview
Hydrology example (mainly maps)
EDINA datasets (via. Digimap)
o Land Cover Map 2007
o Digital Terrain Model (DTM) – OS Profile
o OS Strategi
Other datasets
o Catchment shapefiles (Centre for Ecology & Hydrology / CEH)
o River flows data (HiFlows / Environment Agency)
8. Project aims
Aim of project was to calculate a regression-based equation for PROPWET (soil moisture value)
Currently engineers use constant value for catchment PROPWET (Flood Estimation Handbook)
Test & develop a regression-based equation using GIS, following initial Halcrow analysis
Particular relevance to natural flood management
9. Natural Flood Management (NFM)
NFM involves mitigating flooding using nature, currently difficult to analyse
Increasing flooding (climate change) and increasing costs
Regression-based PROPWET allows simulations of different ground conditions e.g. increased
vegetation
SEPA projects eg Eddleston Water, Scottish Borders
10. NFM example: Eddleston Water
69km2 waterway, part of the River Tweed Catchment
Straightened to improve land drainage leading to habitat loss and increased flood risk, 589
properties at risk of flooding (SEPA Flood Map)
Actions to date: Riparian planting, re-meandering, removal of manmade embankments
11. PROPWET (soil moisture)
Soil moisture important for flood estimates and the PROPortion of time
soils are WET (PROPWET) is an important catchment descriptor
PROPWET range 0-1, e.g. wet catchment 0.8, dry catchment 0.1
PROPWET value currently taken from Flood Estimation Handbook (FEH)
(Centre for Ecology & Hydrology, 1999) based on database query
FEH PROPWET is a single value for a catchment and based on when Soil
Moisture Deficit (SMD) ≤6mm during long term average (1961-90)
FEH PROPWET doesn’t consider land use or climate factors
13. Halcrow analysis
Initial regression-based equation proposed based on 43 catchments (Halcrow, 2012)
PROPWET = 0.5365Log10[SAAR – (PE x LAI4.70 x r 0.79 x 2.003E - 5)] - 0.998
14. Project workflow
Literature review
Obtain datasets
Generate river catchments
Zonal statistics, ArcGIS models to iterate – breakdown into different datasets required
Update regression equation
16. Land Cover Map 2007 (LCM2007)
LCM2007 is a parcel-based classification of 23 land cover classes based on the UK’s terrestrial Broad
Habitats
Classifying summer-winter composite images captured by satellite sensors with 20-30m pixels
LCM represents land cover type and not land use, although the two are often synonymous in practise
17. Land Cover Map 2007 (LCM2007)
Obtained vector & 1km raster (aggregated vector, dominant class per 1x1km pixel)
Vector dataset better metadata but long geo-processing times for country wide queries, vector
manipulated using ogr2ogr
Vector would have been fine in my desktop GIS if had a smaller study area
Raster clipped against river catchments after resampling to 100x100m to obtain better fit, each
catchment containing an average of 9 different land cover types
18. Catchment delineation
Possible to generate river catchments from terrain (DTM)
and ESRI’s Hydrology toolset
1. Digital Terrain Model (DTM)
2. Reclassify noData values
3. Fill ‘holes’ in DTM
4. Flow direction
5. Flow accumulation
6. Stream network definition
7. Catchment extraction
20. Geoprocessing
Used ArcGIS python model builder to iterate zonal statistics tool
Standard Average Annual Rainfall (SAAR), Air Temperature (Ta), Extra-terrestrial
Radiation (Re) per catchment
21. Geoprocessing -> Zonal Stats Maps
Standard Average Annual Rainfall
(SAAR)
Extra-terrestrial radiation (Re)Air temperature (Ta)
23. Leaf Area Index (LAI), Surface resistance (Rsc)
Leaf Area Index: amount vegetation cover
Surface resistance: friction, affects water flow
Assigned classifications using research paper (Hough & Jones, 1997), applied Zonal Statistics
by table tool
25. OS MasterMap Networks - Water Layer
New OS Water Layer (Beta) created with Environment Agency, SEPA, INSPIRE
Mainly derived from OSMM Topo but first time provides dedicated OS Water product with many new
useful attributes for flood modelling / mapping
Features: WatercourseLink (inc Flow Direction), HydroNode, WatercourseLinkSet,
WatercourseSeperatedCrossing, WatercourseInteraction